A Shift in Trust: An Analysis of the Use of AI Chatbots Among People Living With Bipolar Disorder

The Influence of Trust and Need Satisfaction on Young Adults’ Interactions with AI Chatbots for Emotional Support

While ChatGPT was primarily viewed as an efficient tool in a work context, the quantitative survey reveals a weakly significant correlation between psychological stress and openness toward the social-emotional use of chatbots.

Innovative Technologies in Social

Use and Perceptions of AI Chatbots for Mental Health Support Among Adults with Lived Experience

PERCEIVED THERAPEUTIC BOND IN INTERACTIONS WITH MENTAL HEALTH CHATBOTS: A NARRATIVE REVIEW

Background: The rapid development of digital mental health interventions has led to the increasing use of AI-based chatbots as accessible alternatives or complements to traditional psychotherapy. While the therapeutic alliance is a well-established factor influencing treatment outcomes in conventional settings, its applicability to human–chatbot interactions remains unclear. Objective: This narrative review aims to examine existing evidence regarding the subjective experience of therapeutic bond in interactions with mental health chatbots and to explore its characteristics and potential clinical relevance. Methods: A narrative review of selected literature was conducted using databases such as PubMed and Google Scholar. Studies focusing on users’ subjective experiences and relational aspects of interaction with conversational agents were included. Results: Available evidence suggests that users can develop a form of therapeutic bond with mental health chatbots, characterized by perceived empathy, support, and emotional engagement. Both quantitative and qualitative studies indicate that users may attribute human-like qualities to chatbots and describe interactions in relational terms. However, the intensity and nature of this bond vary across individuals, with some users perceiving the interaction as purely instrumental. The bond appears to be limited in depth and may lack key features of traditional therapeutic relationships, such as reciprocity and genuine emotional responsiveness. Conclusions: Therapeutic bond in chatbot-based interventions may represent a distinct and simplified form of relational engagement shaped by the characteristics of human–AI interaction. Further research is needed to assess its long-term development and its relationship to clinical outcomes.

Falling in Love with Algorithms: AI Chatbots and the Transformation of Parasocial Relationships Among Youth

This study explores the formation of quasi-reciprocal parasocial relationship in AI Chatbots mediated intimacy, focusing on how young groups interact with AI and how to view this emotional connection. In the era of rapid development of digital technology and media, AI Chatbots provide fast response with a sense of intimacy, enabling users to experience a sense of reciprocity and emotional value. However, this intimacy experience has not been described in the traditional parasocial relationship. Therefore, this study uses qualitative research methods, including questionnaire method and semi-structured interviews with 12 young participants aged 18 to 25. Finally, according to the interview text, the theme analysis is carried out to explore the participants’ perception of the authenticity and responsiveness of intimacy in AI Chatbots interaction. The study found that although most participants realized that AI Chatbots did not have human emotions at the cognitive level and only relied on Algorithms and databases to answer, they often still experienced a real sense of mutual intimacy. This study extends the traditional parasocial relationship theory by introducing the concept of “quasi-reciprocal interaction”, showing the complex ecology between algorithmic intimacy and emotional experience.

Beyond the Assigned Label: The Predictive Role of AI Acceptance and Disclosure Labels in Mental Health Chatbots

As AI-driven chatbots increasingly support mental health interventions, understanding how their assigned disclosure label influences user experience is crucial. This mixed-methods study ( N  = 187; recruited via Prolific; ages 19–68; 52% female, 48% male - Given the sample’s concentration among Prolific users from South Africa and other English-speaking regions with relatively high digital literacy, transferability to broader clinical, cultural, and digital-access contexts should be interpreted cautiously).employs a randomized experimental design to test whether the assigned disclosure label (AI-Aware vs. Human-Unaware) and advice style (Empathetic vs. Rational), alongside users’ Theory of Change (ToC) and baseline AI acceptance, shape user perceived empathy, satisfaction, and session impact in a single-session mental health chatbot interaction. These null findings should be interpreted as the absence of statistically significant differences between assigned conditions in this specific design, rather than as evidence of equivalence or absence of psychological or relational effects. However, these null results must be interpreted with caution; the absence of explicit manipulation checks limits our ability to definitively conclude that the label successfully altered users’ internal beliefs about the agent’s true identity. In contrast, higher baseline AI acceptance robustly predicted greater perceived empathy and session impact, with an Acceptance × Disclosure interaction for empathy. Qualitatively, topic and sentiment patterns in conversation logs were highly similar across conditions. We argue that, in this brief, online, non-clinical, single-session context, baseline AI acceptance explained more variance in immediate user-reported outcomes than the assigned identity disclosure label. Practically, these findings suggest a hypothesis for future research: pre-intervention education designed to improve user acceptance may benefit deployment efforts, though this was not tested as a causal intervention here.